Dynamic conditional score models with time-varying location, scale and shape parameters
Alvaro Escribano (),
Szabolcs Blazsek () and
Astrid Ayala ()
UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía
We introduce new dynamic conditional score (DCS) models with time-varyinglocation, scale and shape parameters. For these models, we use the Student's-t, GED(general error distribution), Gen-t (generalized-t), Skew-Gen-t (skewed generalized-t),EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverseGaussian) distributions. We show that the maximum likelihood (ML) estimates of thenew DCS models are consistent and asymptotically Gaussian. As an illustration, weuse daily log-return time series data from the S&P 500 index for period 1950 to 2016.We find that, with respect to goodness-of-fit and predictive performance, the DCSmodels with dynamic shape are superior to the DCS models with constant shape andthe benchmark AR-t-GARCH model.
Keywords: Score-driven; shape; parameters; Dynamic; conditional; score; models (search for similar items in EconPapers)
JEL-codes: C58 C52 C22 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:25043
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